point cloud

The test area of investigation is Unterfranken, comprising some 9.000 km2 unhandled point cloud LiDAR data from the state of Bavaria. The dataset specifications, are; 4 ppsm , finished filtrated DTM as grid width of 1m, ≤ 0.2m height accuracy, and ca. ± 0.5m overall positional accuracy. The dataset constitutes of first and last echoes, structured as a binary-grid with the height reference system of DHHN92.

Algorithms and processing methods of the dataset will later in the process also be implemented to other areas of Europe in order to test means of proper large scale processing in shifting spatial contexts. On the same notion, further datasets will be acquired continuously and processed in order to test different levels of analysis potential in regards to e.g. amount of manipulated information detectable after point cloud interpolatio. Accuracy of points per square meter (ppsm) and point density effect on detection procedures will have priority and will be acutely assessed.

Further, detected features and areas of interest will be correlated with information of geophysics, geology, and pedology, consecutively resulting in several elaborate systems of GIS and geostatistical analysis for detection and predictive modelling.

ALS and TLS

Large scale archaeological surveying and prospection is a difficult, highly disputed and problematic field within archaeology (Vosselmann 2010). However, with the incorporation of ALS there is a viable solution for quantitative macro scaled airborne assessment, and furthermore with micro scaled documentation and assessment through TLS (Doneus & Briese 2006). Such a dual process can help assist in managing and identifying cultural heritage within a vast variety of different landscapes (cf. Doneus et al. 2010).

By logic of acquisition techniques and resolution outcome, ALS data are presently more purposeful for macro scaled analysis, whereas TLS data are more local by virtue. Naturally, these boundaries are only stipulated by present notions of scanning logistics and technological capabilities to different interpolated levels by scale of view in relation to resolution and points per square meter (ppsm). The project in present focus will use ALS for macro-scaled analysis and TLS for local comparison on site specific details of objects, structures, and landscape.

Prospection by LiDAR has become a widely used tool within archaeology (Cowling 2011), but utilization of point cloud data for Digital Elevation Models (DEM) and Digital Terrain Models (DTM) do not produce objective truth (cf. Zakšek et al. 2011; Hesse 2014). Hence, data acquisition and manipulation needs to be fully understood in order to produce standardised output and normalised comparable data. Otherwise, filtration and interpolation might generate misleading data artefacts or remove and hide archaeological structures due to diverging definitions of landscape (Crutchley 2010; Deveroux et al 2008). Especially in areas not understood based on ground-truth, such as remote and forested areas not easily accessible, it is important to understand the capabilities and potential misleading information in the data. Thus, controlled benchmark studies of scale of view and degree of information helps general understanding of areas of slope and dense forest vegetation (see Raun et al. in press).

Burial mound Cemetery near Oberhausen, Unterfranken. One burial mound is highlighted in yellow in the point cloud, and the same burial can be seen in the black and white interpolated imagery in the lower right corner.